13 citations · 26 across the 15 of their papers we have counts for
15 papers
Rare Disease Differential Diagnosis with Large Language Models at Scale: From Abdominal Actinomycosis to Wilson's Disease
Elliot Schumacher, Dhruv Naik, Anitha Kannan
Large language models (LLMs) have demonstrated impressive capabilities in disease diagnosis. However, their effectiveness in identifying rarer diseases, which are inherently more c…
Extrinsically-Focused Evaluation of Omissions in Medical Summarization
Elliot Schumacher, Daniel Rosenthal, Dhruv Naik +4
Large language models (LLMs) have shown promise in safety-critical applications such as healthcare, yet the ability to quantify performance has lagged. An example of this challenge…
Injecting knowledge into language generation: a case study in auto-charting after-visit care instructions from medical dialogue
Maksim Eremeev, Ilya Valmianski, Xavier Amatriain +1
Factual correctness is often the limiting factor in practical applications of natural language generation in high-stakes domains such as healthcare. An essential requirement for ma…
Generating medically-accurate summaries of patient-provider dialogue: A multi-stage approach using large language models
Varun Nair, Elliot Schumacher, Anitha Kannan
A medical provider's summary of a patient visit serves several critical purposes, including clinical decision-making, facilitating hand-offs between providers, and as a reference f…
Dialogue-Contextualized Re-ranking for Medical History-Taking
Jian Zhu, Ilya Valmianski, Anitha Kannan
AI-driven medical history-taking is an important component in symptom checking, automated patient intake, triage, and other AI virtual care applications. As history-taking is extre…
CONSCENDI: A Contrastive and Scenario-Guided Distillation Approach to Guardrail Models for Virtual Assistants
Albert Yu Sun, Varun Nair, Elliot Schumacher +1
A wave of new task-based virtual assistants has been fueled by increasingly powerful large language models (LLMs), such as GPT-4 (OpenAI, 2023). A major challenge in deploying LLM-…
DERA: Enhancing Large Language Model Completions with Dialog-Enabled Resolving Agents
Varun Nair, Elliot Schumacher, Geoffrey Tso +1
Large language models (LLMs) have emerged as valuable tools for many natural language understanding tasks. In safety-critical applications such as healthcare, the utility of these…
Learning functional sections in medical conversations: iterative pseudo-labeling and human-in-the-loop approach
Mengqian Wang, Ilya Valmianski, Xavier Amatriain +1
Medical conversations between patients and medical professionals have implicit functional sections, such as "history taking", "summarization", "education", and "care plan." In this…
OSLAT: Open Set Label Attention Transformer for Medical Entity Retrieval and Span Extraction
Raymond Li, Ilya Valmianski, Li Deng +2
Medical entity span extraction and linking are critical steps for many healthcare NLP tasks. Most existing entity extraction methods either have a fixed vocabulary of medical entit…
Adding more data does not always help: A study in medical conversation summarization with PEGASUS
Varun Nair, Namit Katariya, Xavier Amatriain +2
Medical conversation summarization is integral in capturing information gathered during interactions between patients and physicians. Summarized conversations are used to facilitat…
MEDCOD: A Medically-Accurate, Emotive, Diverse, and Controllable Dialog System
Rhys Compton, Ilya Valmianski, Li Deng +4
We present MEDCOD, a Medically-Accurate, Emotive, Diverse, and Controllable Dialog system with a unique approach to the natural language generator module. MEDCOD has been developed…
Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization
Bharath Chintagunta, Namit Katariya, Xavier Amatriain +1
In medical dialogue summarization, summaries must be coherent and must capture all the medically relevant information in the dialogue. However, learning effective models for summar…
Medical symptom recognition from patient text: An active learning approach for long-tailed multilabel distributions
Ali Mottaghi, Prathusha K Sarma, Xavier Amatriain +2
We study the problem of medical symptoms recognition from patient text, for the purposes of gathering pertinent information from the patient (known as history-taking). A typical pa…
Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures
Anirudh Joshi, Namit Katariya, Xavier Amatriain +1
Understanding a medical conversation between a patient and a physician poses a unique natural language understanding challenge since it combines elements of standard open ended con…
Classification As Decoder: Trading Flexibility For Control In Neural Dialogue
Sam Shleifer, Manish Chablani, Namit Katariya +2
Generative seq2seq dialogue systems are trained to predict the next word in dialogues that have already occurred. They can learn from large unlabeled conversation datasets, build a…